利用径向基函数神经网络对中学生学习成绩进行机器学习预测

IF 3.4 Q2 NEUROSCIENCES Trends in Neuroscience and Education Pub Date : 2022-12-01 DOI:10.1016/j.tine.2022.100190
Olusola A. Olabanjo , Ashiribo S. Wusu , Mazzara Manuel
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引用次数: 8

摘要

学习成绩预测模型已成为高校管理人员、辅导人员和教学人员素质提高的有效工具。目的本研究的目的是开发一个径向基函数神经网络,利用学生过去的学习成绩以及他们的认知和精神运动能力来预测学生的表现。方法从一个中学信息库中获取学生的学业、认知和精神运动成绩。使用预处理后的数据集对RBFNN模型进行训练。本文还测量了主成分分析对模型性能的影响。结果该方法的灵敏度为93.49%,特异性为75%,总体准确率为86.59%,AUC评分(在可能的分类阈值上的表现总和)为94%。结论心理运动能力和认知能力对学生的学习成绩也有预测作用。这项研究帮助学生、家长和老师在参加考试之前就对学业成功有一个预测。
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A machine learning prediction of academic performance of secondary school students using radial basis function neural network

Background

Predictive models for academic performance forecasting have been a useful tool in the improvement of the administrative, counseling and instructional personnel of academic institutions.

Aim

The aim of this work is to develop a Radial Basis Function Neural Network for prediction of students’ performance using their past academic records as well as their cognitive and psychomotor abilities.

Methods

We obtained data from a secondary school repository containing academic, cognitive and psychomotor scores of the students. The preprocessed dataset was used to train the RBFNN model. The impact of Principal Component Analysis on the model performance was also measured.

Results

The results gave a sensitivity (pass prediction) of 93.49%, specificity (failure prediction) of 75%, overall accuracy of 86.59% and an AUC score (aggregate measure of performance across the possible classification thresholds) of 94%.

Conclusion

We established in this study that psychomotor and cognitive abilities also predict students’ performance. This study helps students, parents and teachers to get a projection of academic success even before sitting for the examination.

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来源期刊
CiteScore
6.30
自引率
6.10%
发文量
22
审稿时长
65 days
期刊最新文献
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